Semiconductor quantum dots (QDs) are of interest in many application areas due to their electronic properties. Transmission electron microscopy (TEM) images can be used to examine the QD geometry, distribution and strain profile, which will be helpful in the fabrication of QDs with specific electronic properties.

In order to link the contrasts in TEM images with shapes and concentration of these QDs it is crucial to combine strain calculations with TEM image simulations. In collaboration with researchers from TU Berlin, we recently presented a mathematical model and a tool chain for the numerical simulation of TEM images for semiconductor QDs, published in Optical and Quantum Electronics (Numerical simulation of TEM images for In(Ga)As/GaAs quantum dots with various shapes). We simulated lens-shaped and pyramidal indium gallium arsenide QDs embedded in a gallium arsenide matrix and compared the resulting TEM images to experimental ones. This tool chain will be applied to generate a database of simulated TEM images, which is a key element of a novel concept for model-based geometry reconstruction of semiconductor QDs, involving machine learning techniques.

This work was supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy—MATH+ (Project EF3-1) and CRC 787 “Semiconductor Nanophotonics” under Project A4.